Computer system and method for processing data relating to nurse intervention program decisions

ABSTRACT

According to some embodiments, historical claim data may be stored in a computer storage unit. The historical claim data may be used to train and verify a predictive model, the predictive model being associated with an evaluation of claim transactions to determine whether to refer each claim transactions to a nurse intervention program. Data for current claim transactions may be processed, and the trained and verified predictive model may be applied to the data for the current claim transactions to generate a respective output for each of the current claim transactions. Some of the current claim transactions may be selectively routed to a case management center associated with the nurse intervention program based on the outputs generated by the predictive model.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 12/634,046 entitled System and Method Using aPredictive Model for Nurse Intervention Program Decisions, filed Dec. 9,2009, the entire contents of which is herein incorporated by referencefor all purposes.

FIELD

The present invention relates to computer systems and more particularlyto computer systems that apply predictive models in connection withnurse intervention program decisions.

BACKGROUND

A nurse intervention program may be used to improve the health careservices provided to an injured worker. Consider, for example, a workerwho submits a workers compensation claim as a result of an injury he orshe received while at work. In such a situation, an insurer mightarrange for a nurse to visit and provide additional health care to theworker while he or she recovers at home. Note that the improved carecould result in the worker returning to his or job sooner (as comparedto a worker with a similar injury who did not receive additional healthcare). As a result, the insurer can also benefit from providing improvedhealth care (e.g., because the worker returned to work sooner and thus atotal amount of benefits paid by the insurer might be reduced).Determining when it is appropriate to utilize a nurse interventionprogram, however, can be a difficult and time consuming task. Forexample, the decision might depend on a nature of the injury as well asdetails about the particular claimant. Moreover, an insurer might handleclaims associated with many different accounts, and each account mighthave different rules and benefits that need to be considered whendetermining whether or not a nurse intervention program is appropriatefor a particular claim.

Although some systems have attempted to apply simple rules to helpdetermine whether or not a nurse intervention program is appropriate fora particular claim, such approaches may not effectively identify whichpatients and/or injuries may benefit most from these services.

The present inventors have recognized the utility of a predictive modelfor determining that certain insurance claims should be identified andreferred for a nurse intervention program. Moreover, the use of thepredictive model can be significantly enhanced by expanding the types ofinformation used to train the predictive model and included in currentclaim transaction data to which the predictive model is applied, as wellas by considering account-specific information associated with aparticular insurance claim.

SUMMARY

A computer system is disclosed which includes a data storage module toreceive, store and provide access to historical claim transactions andto current claim transactions. The computer system further includes apredictive model component that is coupled to the data storage moduleand determines whether to identify a given one of the current claimtransactions for referral to a nurse intervention program.

The computer system also includes a computer processor that executesprogrammed instructions and stores and retrieves the data related tocurrent claim transactions. Further included in the computer system is aprogram memory, coupled to the computer processor, and which storesprogram instruction steps for execution by the computer processor.

A model training component is also included in the computer system. Themodel training component is coupled to the computer processor and trainsthe predictive model component based on the data related to thehistorical claim transactions in accordance with program instructionsstored in the program memory and executed by the computer processor. Asa consequence, a trained predictive model component is provided.

Still further included in the computer system is an output device. Theoutput device is coupled to the computer processor and outputs an outputindicative of whether the current claim transaction in question shouldbe referred to a nurse intervention program. The computer processorgenerates the output in accordance with program instructions in theprogram memory and executed by the computer processor. The output isgenerated in response to application of data for the current claimtransaction in question to the trained predictive model component. Thecomputer system further includes a routing module which directs workflowbased on the output from the output device.

The present inventors have observed that effective indicators in aninsurance claim file that a claim may be beneficially referred to anurse intervention program. By incorporating this data, or informationextracted therefrom, in historical data used to train a predictivemodel, and in current transaction information applied to the predictivemodel, the performance of the predictive model may be enhanced, and theability of the model to recommend nurse intervention may be improved. Asa result, improved decisions associated with a nurse interventionprogram may be provided.

With these and other advantages and features of the invention that willbecome hereinafter apparent, the invention may be more clearlyunderstood by reference to the following detailed description of theinvention, the appended claims, and the drawings attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a partially functional block diagram that illustrates aspectsof a computer system provided in accordance with some embodiments of theinvention.

FIG. 2 is a block diagram that illustrates a computer that may form allor part of the system of FIG. 1.

FIG. 3 is a block diagram that provides another representation ofaspects of the system of FIG. 1.

FIGS. 4A and 4B are a flow chart that illustrates a process that may beperformed in the computer system of FIGS. 1-3.

FIG. 5 is a flow chart that illustrates aspects of the process of FIGS.4A and 4B according to some embodiments.

FIG. 6 is a flow chart that illustrates aspects of the process of FIGS.4A and 4B in accordance with some embodiments.

FIG. 7 is a flow chart illustrating a case management process for anurse intervention program.

FIG. 8 is a flow chart illustrating how a predictive model might betrained according to some embodiments.

FIG. 9 illustrates predictive model inputs according to someembodiments.

FIG. 10 is a more detailed example of particular types of predictivemodel inputs in accordance with some embodiments.

DETAILED DESCRIPTION

In general, and for the purposes of introducing concepts of embodimentsof the present invention, a computer system incorporates a “predictivemodel.” As used herein, the phrase “predictive model” might refer to,for example, any of a class of algorithms that are used to understandrelative factors contributing to an outcome, estimate unknown outcomes,discover trends, and/or make other estimations based on a data set offactors collected across prior trials. Note that a predictive modelmight refer to, but is not limited to, methods such as ordinary leastsquares regression, logistic regression, decision trees, neuralnetworks, generalized linear models, and/or Bayesian models. Thepredictive model is trained with historical claim transaction data, andis applied to current claim transactions to determine whether thecurrent claim transactions should be referred to a nurse interventionprogram. Both the historical claim transaction data and datarepresenting the current claim transactions might include, according tosome embodiments, indeterminate data or information extracted therefrom.For example, such data/information may come from narrative and/ormedical text notes associated with a claim file.

Features of some embodiments of the present invention will now bedescribed by first referring to FIG. 1. FIG. 1 is a partially functionalblock diagram that illustrates aspects of a computer system 100 providedin accordance with some embodiments of the invention. For presentpurposes it will be assumed that the computer system 100 is operated byan insurance company (not separately shown) for the purpose of referringcertain claims to a nurse intervention program when appropriate. Theclaims may be from one or more different lines of insurance, such asworker's compensation, property and casualty, etc.

The computer system 100 includes a data storage module 102. In terms ofits hardware the data storage module 102 may be conventional, and may becomposed, for example, by one or more magnetic hard disk drives. Afunction performed by the data storage module 102 in the computer system100 is to receive, store and provide access to both historical claimtransaction data (reference numeral 104) and current claim transactiondata (reference numeral 106). As described in more detail below, thehistorical claim transaction data 104 is employed to train a predictivemodel to provide an output that indicates whether a claim should bereferred to a nurse intervention program, and the current claimtransaction data 106 is thereafter analyzed by the predictive model.Moreover, as time goes by, and results become known from providing nurseinterventions for current claim transactions, at least some of thecurrent claim transactions may be used to perform further training ofthe predictive model. Consequently, the predictive model may therebyadapt itself to changing patterns of claims.

Either the historical claim transaction data 104 or the current claimtransaction data 106 might include, according to some embodiments,determinate and indeterminate data. As used herein and in the appendedclaims, “determinate data” refers to verifiable facts such as the dateof birth, age or name of a claimant or name of another individual or ofa business or other entity; a type of injury; a medical diagnosis; adate of loss, or date of report of claim, or policy date or other date;a time of day; a day of the week; a vehicle identification number, ageographic location; and a policy number.

As used herein and in the appended claims, “indeterminate data” refersto data or other information that is not in a predetermined formatand/or location in a data record or data form. Examples of indeterminatedata include narrative speech or text, information in descriptive notesfields and signal characteristics in audible voice data files.Indeterminate data extracted from medical notes might be associatedwith, for example, a prior injury, alcohol related co-morbidityinformation, drug related co-morbidity information, tobacco relatedco-morbidity information, arthritis related co-morbidity information,diabetes related co-morbidity information, and/or obesity relatedco-morbidity information.

The determinate data may come from one or more determinate data sources108 that are included in the computer system 100 and are coupled to thedata storage module 102. The determinate data may include “hard” datalike the claimant's name, date of birth, social security number, policynumber, address; the date of loss; the date the claim was reported, etc.One possible source of the determinate data may be the insurancecompany's policy database (not separately indicated). Another possiblesource of determinate data may be from data entry by the insurancecompany's claims intake administrative personnel.

The indeterminate data may originate from one or more indeterminate datasources 110, and may be extracted from raw files or the like by one ormore indeterminate data capture modules 112. Both the indeterminate datasource(s) 110 and the indeterminate data capture module(s) 112 may beincluded in the computer system 100 and coupled directly or indirectlyto the data storage module 102. Examples of the indeterminate datasource(s) 110 may include data storage facilities for document images,for text files (e.g., claim handlers' notes) and digitized recordedvoice files (e.g., claimants' oral statements, witness interviews, claimhandlers' oral notes, etc.). Examples of the indeterminate data capturemodule(s) 112 may include one or more optical character readers, aspeech recognition device (i.e., speech-to-text conversion), a computeror computers programmed to perform natural language processing, acomputer or computers programmed to identify and extract informationfrom narrative text files, a computer or computers programmed to detectkey words in text files, and a computer or computers programmed todetect indeterminate data regarding an individual. For example, claimhandlers' opinions may be extracted from their narrative text filenotes.

The computer system 100 also may include a computer processor 114. Thecomputer processor 114 may include one or more conventionalmicroprocessors and may operate to execute programmed instructions toprovide functionality as described herein. Among other functions, thecomputer processor 114 may store and retrieve historical claimtransaction data 104 and current claim transaction data 106 in and fromthe data storage module 102. Thus the computer processor 114 may becoupled to the data storage module 102.

The computer system 100 may further include a program memory 116 that iscoupled to the computer processor 114. The program memory 116 mayinclude one or more fixed storage devices, such as one or more hard diskdrives, and one or more volatile storage devices, such as RAM (randomaccess memory). The program memory 116 may be at least partiallyintegrated with the data storage module 102. The program memory 116 maystore one or more application programs, an operating system, devicedrivers, etc., all of which may contain program instruction steps forexecution by the computer processor 114.

The computer system 100 further includes a predictive model component118. In certain practical embodiments of the computer system 100, thepredictive model component 118 may effectively be implemented via thecomputer processor 114, one or more application programs stored in theprogram memory 116, and data stored as a result of training operationsbased on the historical claim transaction data 104 (and possibly alsodata resulting from training with current claims that have benefittedfrom nurse interventions). In some embodiments, data arising from modeltraining may be stored in the data storage module 102, or in a separatedata store (not separately shown). A function of the predictive modelcomponent 118 may be to identify current claim transactions that shouldbe referred to a nurse intervention program. The predictive modelcomponent may be directly or indirectly coupled to the data storagemodule 102.

The predictive model component 118 may operate generally in accordancewith conventional principles for predictive models, except, as notedherein, for at least some of the types of data to which the predictivemodel component is applied. Those who are skilled in the art aregenerally familiar with programming of predictive models. It is withinthe abilities of those who are skilled in the art, if guided by theteachings of this disclosure, to program a predictive model to operateas described herein.

Still further, the computer system 100 includes a model trainingcomponent 120. The model training component 120 may be coupled to thecomputer processor 114 (directly or indirectly) and may have thefunction of training the predictive model component 118 based on thehistorical claim transaction data 104. (As will be understood fromprevious discussion, the model training component 120 may further trainthe predictive model component 118 as further relevant claim transactiondata becomes available.) The model training component 120 may beembodied at least in part by the computer processor 114 and one or moreapplication programs stored in the program memory 116. Thus the trainingof the predictive model component 118 by the model training component120 may occur in accordance with program instructions stored in theprogram memory 116 and executed by the computer processor 114.

In addition, the computer system 100 may include an output device 122.The output device 122 may be coupled to the computer processor 114. Afunction of the output device 122 may be to provide an output that isindicative of whether (as determined by the trained predictive modelcomponent 118) a particular one of the current claim transactions shouldbe referred to a nurse intervention program. The output may be generatedby the computer processor 114 in accordance with program instructionsstored in the program memory 116 and executed by the computer processor114. More specifically, the output may be generated by the computerprocessor 114 in response to applying the data for the current claimtransaction to the trained predictive model component 118. The outputmay, for example, be a true/false flag or a number within apredetermined range of numbers. In some embodiments, the output devicemay be implemented by a suitable program or program module executed bythe computer processor 114 in response to operation of the predictivemodel component 118.

Still further, the computer system 100 may include a routing module 124.The routing module 124 may be implemented in some embodiments by asoftware module executed by the computer processor 114. The routingmodule 124 may have the function of directing workflow based on theoutput from the output device. Thus the routing module 124 may becoupled, at least functionally, to the output device 122. In someembodiments, for example, the routing module may direct workflow byreferring, to a nurse intervention program 126, current claimtransactions analyzed by the predictive model component 118 and found tobe questionable. In particular, the questionable current claimtransactions may be referred to case manager 128 who is associated withthe nurse intervention program 126. The nurse intervention program 126may be a part of the insurance company that operates the computer system100, and the case manager 128 might be an employee of the insurancecompany.

FIG. 2 is a block diagram that illustrates a computer 201 that may formall or part of the system 100 of FIG. 1. As depicted, the computer 201includes a computer processor 200 operatively coupled to a communicationdevice 202, a storage device 204, an indeterminate data capture device206, one or more other input devices 207 and an output device 208.Communication device 202 may be used to facilitate communication with,for example, other devices (such as personal computers—not shown in FIG.2—assigned to individual employees of the insurance company). Theindeterminate data capture device 206 may include any one or more of thedevices referred to above in connection with block 112 in FIG. 1 and mayhave the functions ascribed above to those devices. The other inputdevice(s) 207 may comprise, for example, a keyboard, a keypad, a mouseor other pointing device, a microphone, knob or a switch, an infra-red(IR) port, a docking station, and/or a touch screen. The other inputdevice(s) 207 may be used, for example, to enter information. Outputdevice 208 may comprise, for example, a display (e.g., a display screen)a speaker, and/or a printer.

Storage device 204 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g.,magnetic tape and hard disk drives), optical storage devices, and/orsemiconductor memory devices such as Random Access Memory (RAM) devicesand Read Only Memory (ROM) devices.

Storage device 204 stores one or more programs or portions of programs(at least some of which being indicated by blocks 210-214) forcontrolling processor 200. Processor 200 performs instructions of theprograms, and thereby operates in accordance with the present invention.In some embodiments, the programs may include a program or programmodule 210 that acts as a predictive model for determining whether torefer current claim transactions to a nurse intervention program. Thetraining function for the predictive model 210 is not indicatedseparately in FIG. 2 from the predictive model itself.

Another program or program module stored on the storage device 204 isindicated at block 212 and is operative to allow the computer 201 toroute or refer current claim transactions to insurance company employeesas appropriate based on the results obtained by applying the predictivemodel 210 to the data which represents the current claim transactions.

Still another program or program module stored on the storage device 204is indicated at block 214 and engages in database management and likefunctions related to data stored on the storage device 204. There mayalso be stored in the storage device 204 other software, such as one ormore conventional operating systems, device drivers, communicationssoftware, etc. The historical claim transaction data 104 and the currentclaim transaction data 106, as previously described with reference toFIG. 1, are also shown in FIG. 2 as being stored on the storage device204.

FIG. 3 is another block diagram that presents the computer system 100 ina somewhat more expansive or comprehensive fashion (and/or in a morehardware-oriented fashion).

The computer system 100, as depicted in FIG. 3, includes the computer201 of FIG. 2. The computer 201 is depicted as a “referral server” inFIG. 3, given that a function of the computer 201 is to selectivelyrefer current claim transactions to a nurse intervention program of theinsurance company. As seen from FIG. 3, the computer system 100 mayfurther include a conventional data communication network 302 to whichthe computer/referral server 201 is coupled.

FIG. 3 also shows, as parts of computer system 100, data input device(s)304 and data source(s) 306, the latter (and possibly also the former)being coupled to the data communication network 302. The data inputdevice(s) 304 and the data source(s) 306 may collectively include thedevices 108, 110 and 112 discussed above with reference to FIG. 1. Moregenerally, the data input device(s) 304 and the data source(s) 306 mayencompass any and all devices conventionally used, or hereafter proposedfor use, in gathering, inputting, receiving and/or storing informationfor insurance company claim files.

Still further, FIG. 3 shows, as parts of the computer system 100,personal computers 308 assigned for use by nurses (who are members ofthe insurance company's nurse intervention program) and personalcomputers 310 assigned for use by case managers (who might also bemembers of or otherwise associated with the nurse intervention program).The personal computers 308, 310 are coupled to the data communicationnetwork 302.

Also included in the computer system 100, and coupled to the datacommunication network 302, is an electronic mail server computer 312.The electronic mail server computer 312 provides a capability forelectronic mail messages to be exchanged among the other devices coupledto the data communication network 302. Thus the electronic mail servercomputer 312 may be part of an electronic mail system included in thecomputer system 100.

The computer system 100 may also be considered to include furtherpersonal computers (not shown), including, e.g., computers which areassigned to individual claim handlers or other employees of theinsurance company.

FIG. 4A is a flow chart that illustrates a process that may be performedin the computer system 100/computer 201 of FIGS. 1-3. At 402 in FIG. 4A,determinate data concerning historical claim transactions is provided tothe computer system 100/computer 201. As indicated above, thedeterminate data may reflect the “hard facts” about the historical claimtransactions. In addition, the determinate data may include, for eachhistorical claim transaction, an indication as to whether or not thetransaction in question benefited from a nurse intervention program. Itwill be understood that the historical claim transactions relate toclaims made during a past period of time or periods of time. Thisinformation may, for example, be loaded into the computer 201 from adatabase and/or archive of claim files.

At 404, the determinate historical claim transaction data may be storedin the computer system 100/computer 201. At 406, indeterminate data forthe historical claim transactions is provided to the computer system100/computer 201. The indeterminate data may take the form, for example,of images of paper documents, digitized voice recordings, text filesthat reflect claim handlers' case file notes, or text files generated byOCR-processing paper documents or document images, or text filesgenerated by speech-to-text conversion of digitized voice recordings. At408, the indeterminate historical claim transaction data may be storedin the computer system 100/computer 201.

At 410 the computer system 100/computer 201 performs processing toextract significant information from the stored indeterminate historicalclaim transaction data. The extraction of information from theindeterminate historical claim transaction data may take one or more ofa number of different forms. For example, the computer system100/computer 201 may perform key word searches on text files included inthe indeterminate historical claim transaction data. As still anotherexample, names of entities (e.g., the treating physician's name) may beextracted from text files (e.g., claimant's attorney's demand packageafter OCR scanning) by entity analysis of the text files. One example ofnatural language processing may operate to mine claim handlers' opinionsfrom the narrative text notes. At 412 in FIG. 4, the informationextracted at 410 from the indeterminate historical claim transactiondata is stored by the computer system 100/computer 201.

At 414, two mutually exclusive subsets of the historical claimtransactions are formed. One of the two subsets is to be used fortraining the predictive model. The other of the two subsets is to beused for verifying the predictive model after it has been trained.

At 416, the predictive model is trained using the first one of thesubsets of historical claim transaction data. In some embodiments, forexample, the predictive model may be trained by using one or more typesof regression analysis. In addition or alternatively, other types ofpattern detection analysis may be applied to the first subset ofhistorical claim transaction data. As another alternative, thepredictive model may be implemented as a neural network. In someembodiments, the predictive model is of a kind that, for each claimtransaction (after training) to which it is applied, the model generatesa numerical output within a certain range. The output may be generatedin such a manner that a higher output implies a higher likelihood thatit would be worthwhile to refer the claim for nurse intervention.

After training of the predictive model, it is verified, as indicated at418, by applying the model to the second subset of historical claimtransaction data. The results of the verification processing areanalyzed to confirm that the predictive model performs effectively ingenerally assigning higher outputs to the claim transactions in thesecond subset that actually benefited from the nurse interventionprogram.

At 420 in FIG. 4A, determinate data concerning current claimtransactions is provided to the computer system 100/computer 201.Preferably the determinate data for the current claim transactions is ofthe same kinds as the determinate data for the historical claimtransactions, as described above in connection with step 402. Thedeterminate data may be loaded into the computer 201 from a database ofcurrent claim information. At 422, the determinate current claimtransaction data may be stored in the computer system 100/computer 201.

At 424, indeterminate data for the current claim transactions isprovided to the computer system 100/computer 201. Preferably theindeterminate data for the current claim transactions is of the samekinds as the indeterminate data for the historical claim transactions,as described above in connection with step 406. At 426, theindeterminate current claim transaction data may be stored in thecomputer system 100/computer 201.

At 428, the computer system 100/computer 201 performs processing toextract significant information from the stored indeterminate currentclaim transaction data. Preferably this is done in the same manner asthe information extraction described above in connection with step 410.At 430, the information extracted at 428 from the indeterminate currentclaim transaction data is stored by the computer system 100/computer201.

Referring now to FIG. 4B, at 432 the predictive model is applied to thedeterminate data, and to the information extracted at 428 from theindeterminate data, for one of the current claim transactions. (Onecould also say that the determinate data and the information extractedfrom the indeterminate data are applied to the predictive model.) Theoperation of the predictive model results in an output being generatedfor the current claim transaction in question. As indicated by previousdiscussion, the output is indicative of a determination by thepredictive model as to the likelihood that the current claim wouldbenefit from the nurse intervention program, with a higher outputindicating a greater likelihood that the claim will benefit.

At 434 in FIG. 4, the computer 201 makes a routing decision with respectto the current claim transaction applied to the predictive model at 432.This decision is based on the output generated from the predictive modelfor the current claim transaction in question. From ensuing discussion,it will be understood that the routing decision may be whether to referthe current claim transaction in question to the insurance company'snurse intervention program.

FIGS. 5 and 6 are flow charts that illustrate additional details of theprocess of FIGS. 4A and 4B. Referring initially to FIG. 5, at 502 datafor the next (or first) current claim transaction in a batch oftransactions to be screened is accessed. Then, at decision block 504 thecomputer system 100/computer 201 determines whether the current claimtransaction accessed at 502 qualifies for consideration for referral toa nurse intervention program. That is, certain current claimtransactions may fall into categories such that the current claimtransaction is not worth analyzing by the predictive model. Examples ofcurrent claim transactions that are will not benefit from nurseintervention may include, for example, worker's compensation claimsarising from loss of life on the job or claims associated with anaccount (e.g., an employer) that does not provide such a benefit toemployees. Thus it may be said that decision block 504 performs a“pre-screening” relative to the current claim transaction accessed atblock 502.

If a determination is made at decision block 504 to the effect that itmakes sense to apply the current claim transaction accessed at 502 tothe predictive model, then block 506 may follow decision block 504. Atblock 506, the current claim transaction accessed at 502 is placed in aqueue for application of the predictive model. Then decision block 508follows block 506. At decision block 508, it is determined whether anymore current claim transactions are awaiting analysis by the predictivemodel. If so, then the process of FIG. 5 loops back to block 502, andthe next current claim transaction awaiting screening is accessed andsubjected to “pre-screening” at decision block 504. However, if atdecision block 508 it is determined that no further current claimtransactions are awaiting analysis by the predictive model, then theprocess of FIG. 5 ends.

Considering again decision block 504, if a determination is made at thatpoint that the current claim transaction accessed at block 502 is of akind such that it does not make sense to apply the predictive model,then, per branch 510, the process of FIG. 5 skips from decision block504 to decision block 508, omitting block 506 with respect to thecurrent claim transaction accessed at block 502.

Thus, in such cases, the current claim transaction accessed at block 502is not queued for application of the predictive model and is notscreened for possible referral to the nurse intervention program.

Referring now to FIG. 6, the process illustrated therein begins withdecision block 602. At decision block 602 the computer system100/computer 201 determines whether there are any current claimtransactions in the queue referred to in connection with block 506 ofFIG. 5. If there is at least one current claim transaction in the queue,then block 604 follows decision block 602. At 604, the computer system100/computer 201 accesses the next current claim transaction in thequeue. Then, at 606, the computer system 100/computer 201 applies thepredictive model to the current claim transaction accessed at 604. Fromprevious discussion it will be recognized that the application of thepredictive model to the current claim transaction in question results inthe computer system 100/computer 201 generating an output for thecurrent claim transaction in question, where the output is indicative ofwhether it is advisable that the current claim transaction in questionshould be referred to a nurse intervention program.

Decision block 608 follows block 606. At decision block 608, thecomputer system 100/computer 201 determines whether the output generatedat 606 exceeds a predetermined threshold. The threshold may, forexample, have been set at the time that the training of the predictivemodel was verified (block 418, FIG. 4A). The threshold may have been setin such a manner as to balance the potentially conflicting goals ofavoiding false positive indications, while avoiding false negativedeterminations. The achievement of this balance may reflect therespective levels of outputs generated by the predictive model duringverification processing to proper and improper historical claimtransactions. According to other embodiments, the output of thepredictive model is simply a “true/false” flag indicating whether or notthe nurse intervention program is appropriate.

If it is determined at decision block 608 that the output generated at606 exceeds the threshold, then block 610 follows decision block 608. Atblock 610, the computer system 100/computer 201 may refer the currentclaim transaction in question to a case manager and/or nurse associatedwith the nurse intervention program. This may be done by the computersystem 100/computer 201 automatically e-mailing the electronic case filefor the claim transaction to a manager. If more than one case manager ornurse is available to receive the referral of the claim transaction,then the computer system 100/computer 201 may automatically select theparty who is to receive the referral based on one or more factors suchas the type of claim, the nurse's qualifications and/or experience, thecase manager's current workload, etc.

In some embodiments, the computer system 100/computer 201 may cause theclaim transactions referred to each case manager or nurse, and/or to thenurse intervention program as a whole, to be queued according to theoutputs generated for the claim transactions. That is, claimtransactions having higher outputs assigned by the predictive model maybe placed higher in the individual case manager or nurse queues and/orin the overall nurse intervention program's queue.

Block 612 may follow block 610. At block 612 the computer system100/computer 201 automatically notifies the claim handler to whom theclaim transaction had been assigned that the claim transaction was beingreferred to the nurse intervention program. At the same time, thecomputer system 100/computer 201 may inform other interested branches ofthe insurance company that the claim transaction is being referred tothe nurse intervention program.

After block 612, the process of FIG. 6 loops back to decision block 602to determine whether there are other claim transactions in the queue tobe analyzed by the predictive model. If, at a point when decision block602 is reached, there are no more claim transactions in the queue, thenthe process of FIG. 6 ends, as indicated at branch 614.

FIG. 7 is a flow chart illustrating a case management process for anurse intervention program. At 702, a triage nurse may receive a filefrom a case manager and/or case management center. The file mightcomprise, for example, an electronic file, workbasket, and/or task listgenerated in response to a notice of injury (e.g., a first notice ofinjury) associated with a worker's compensation claim or a bodily injuryclaim.

At 704, it is determined whether or not the account associated with thefile provides triage as a potential benefit for employees. If not, theprocess simply ends. The determination might be based on, for example,Special Account Instructions (SAI) associated with an employer, union,or other provider of benefits.

If the account does provide triage as a potential benefit for employees,a triage activity may be created at 706. The triage activity might beassociated with, for example, an electronic, automated insuranceprocessing workflow system.

The available information may be reviewed at 708 and contacts may bemade as appropriate to determine additional information. For example, adoctor, employer, and/or injured worker might be contacted to determineadditional details associated with an injury or accident. The additionalinformation might be entered, for example, by a telephonic triage casemanager.

At 708, the available information may also be provided to a predictivemodel as described herein. Note that the predictive model might use anyof a number of different parameters (associated with historical cases orthe case currently being reviewed) to determine if a nurse interventionprogram should be utilized based on a predicted ultimate medical claimseverity and/or a predicted ultimate indemnity claim severity.

For example, the decision might be at least partially based oninformation extracted from narrative text notes (e.g., medical notes)indicating a prior injury, alcohol related co-morbidity information,drug related co-morbidity information, tobacco related co-morbidityinformation, arthritis related co-morbidity information, diabetesrelated co-morbidity information, and/or obesity related co-morbidityinformation. Other types of information that might be utilized includedemographic information (e.g., the age of a worker), injury details,accident information, employment information, and/or financialinformation (e.g., a worker's salary). Still other examples ofinformation might include a Part Of Body (POB) injured, a Nature OfInjury (NOI), an industry or occupation, a medical paid or reservesamount, an indemnity paid or reserves amount, a previously noted injury,a presence of a co-morbidity, and/or a claim complexity. According tosome embodiments, a predictive model may utilize an injury matrix todetermine whether or not nurse intervention is appropriate. For example,different classes of injuries might be assigned different scores orweights when scoring an injury.

If triage is not recommended at 710, the process ends. If triage isrecommended, it is determined whether the account associated with thefile needs to approve triage before it is provided to a claimant. Ifapproval is required and the account declines to approve the file at712, the process ends. If the account does approve triage (or if theaccount does not require pre-approval), the file may sent to the casemanager for triage at 714.

Note that the processes described herein might be performed more thanonce for a particular injury or claim. For example, the process of FIG.7 could first be performed within three days of an injury and the bere-performed again after another period of time (e.g., twenty five daysafter the injury).

In some embodiments, the nurse intervention program may receivereferrals via other channels than referral based on a predictive model.For example, referrals may also be from claim handlers, or from outsidesources, directly from insurance agents, etc. Other referrals for thenurse intervention may take place selectively/pro-actively based onother sources when appropriate claim transactions are identified.Moreover, in some embodiments, some or all of the above-mentionedcommunications among case managers, claim handlers, and nurses may bevia an automated messaging system, such as the electronic mail systemreferred to above in conjunction with FIG. 3.

According to some embodiments, a predictive model is used to facilitatea provisioning of nurse intervention services. Note that the predictivemodel might be designed and/or trained in a number of different ways.For example, FIG. 8 is a flow chart illustrating how a predictive modelmight be created according to some embodiments. At 802, data to be inputto the predictive model may be analyzed, scrubbed, and/or cleaned. Thisprocess might involve a broad review of the relevant variables that maybe included in the sample data. Variables might be examined for thepresence of erroneous values, such as incorrect data types or valuesthat don't make sense. Observations with such “noisy” data or missingdata may be removed from the sample. Similarly, any data points thatrepresent outliers are also managed.

At 804, a data reduction process might be performed. This might occur,for example, between variables in the data sample and/or within specificvariables. According to some embodiments, certain variables may beassociated with one another and the number of these variables may bereduced. For example, it might be noted that injuries to the leftshoulder generally have values similar to injuries to the rightshoulder. Within certain variables, the raw values may represent a levelof information that is too granular. These raw values might then becategorized to reduce the granularity. A goal of the data reductionprocess may be to reduce the dimensionality of the data by extractingfactors or clusters that may account for the variability in the data.

At 806, any necessary data transformations may be performed.Transformations of dependent and/or independent variables in statisticalmodels can be useful for improving interpretability, model fit, and/oradherence to modeling assumptions. Some common methods may includenormalizations of variables to reduce the potential effects of scale anddummy coding or other numeric transformations of character variables.

Once these steps are complete, the predictive model may be developed at808. Depending on the nature of the desired prediction, various modelingtechniques may be utilized and compared. The list of independentvariables may be narrowed down using statistical methods as well asbusiness judgment. Lastly, the model coefficients and/or weights may becalculated and the model algorithm may be completed. For example, itmight be determined that injuries to a knee are generally twice asserious as injuries to a shoulder (and thus, according to someembodiments, a knee injury might be weighted more as compared to ashoulder injury).

Note that many different types of data might be used to create,evaluate, and/or use a predictive model. For example, FIG. 9 is a blockdiagram of a system 900 illustrating inputs to a predictive model 910according to some embodiments. In this example, the predictive model 910might receive information about prior claims 920 (e.g., historical dataabout prior workers compensation claims). Moreover, the predictive model910 might receive monetary information about claims 930 (e.g., a totalamount of payments made in connection with a claim) and/or demographicinformation 940 (e.g., the age or sex of a claimant). According to someembodiments, claim notes 950 are input to the predictive model 910(e.g., an keywords may be extracted from the notes 950). Other types ofinformation that might be provided to the predictive model 910 includemedical bill information 960 (e.g., including information about medicalcare that was provided to a claimant), injury details 970 (e.g., whichpart or parts of the body have been injured), and employment data 980(e.g., an employee's salary or how long an employee has worked for anemployer).

FIG. 10 is a more detailed system 1000 illustrating in more detailparticular types of inputs that might be provided to a predictive model1010 in accordance with some embodiments. For example, the predictivemodel 1010 might receive, in connection with historical and/or currentclaim transaction, general data 1020 such as demographic information,injury details, accident information, employment information, and/orfinancial information. The predictive model 1010 may also receiveinformation found via text mining 1030, such as information about priorinjuries and/or co-morbidity information associated with a claimant.

Some of the general data 1020 and information determined via text mining1030 may be considered, stored, and/or further processed as “keyfactors” 1040 (e.g., factors that might be of particular relevance whendetermining whether or not utilization of a nurse intervention programis appropriate for a particular claim). For example, a claimant age, anindustry or occupation associated with a claim, an amount of indemnitypaid (and/or an amount of reserves), previously noted injuries, and/orco-morbidity information might be especially important when determiningwhether or not to provide nurse intervention program services for aclaim.

One type of data that may be of particular relevance is injuryinformation associated with an amount and type of care management thatmight be provided for a claimant. According to some embodiments, theinjury information be structured as a matrix or template of potentialinjuries (e.g., and in some embodiments, various elements of the datastructure may be provided with appropriate weighing factors as describedwith respect to FIG. 8). For example, as illustrated in FIG. 10, keyfactors 1040 for a “back” injury might include Nature of Injury (NOI)information (e.g., crushing, fracture, or infection) and Part of Body(POB) information (e.g., thoracic, upper back, or lower back). Similarinformation may be provided for other types of injuries (e.g., wrist,eye, or lung). The general data 1020, text mining information 1030, andkey factors 1040 might then be used by the predictive model 1010 fortraining and/or current claim evaluations.

The process descriptions and flow charts contained herein should not beconsidered to imply a fixed order for performing process steps. Rather,process steps may be performed in any order that is practicable.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

What is claimed is:
 1. A computer system for data processing including asystem for executing a trained predictive model to determine a nurseintervention program score for insurance claims based on at leastinjury, occupation and two or more items of co-morbidity data,determining a nurse intervention program score and determine a positiveor negative nurse intervention program decision based on the score,comprising: a nurse intervention program referral determination computersystem, comprising: a data storage device storing data indicative of atrained predictive model, trained on historical claim transaction data,configured to determine, based on insurance claim transaction data, anurse intervention program score, the score indicative of a likelihoodof suitability for nurse intervention programs providing health careprovided by a nurse to an injured individual, of insurance claimtransactions; one or more computer processors in communication with thedata storage device; and a program memory, coupled to the one or morecomputer processors, storing program instruction steps for execution bythe one or more computer processors, the program instructions, whenexecuted by the one or more computer processors, causing the one or morecomputer processors to: receive data indicative of a plurality ofinsurance claim transactions, the data comprising at least injury data,employee occupation data, and co-morbidity data; apply the predictivemodel to the received data indicative of a plurality of insurance claimtransactions to determine a nurse intervention program score, thepredictive model being responsive to values of each of injury,occupation, and co-morbidity data, the co-morbidity data comprising atleast two of (i) alcohol related co-morbidity information, (ii) drugrelated co-morbidity information, (iii) tobacco related co-morbidityinformation, (iv) arthritis related co-morbidity information, (v)diabetes related co-morbidity information, or (vi) obesity relatedco-morbidity information; and based on the nurse intervention programscore, determine a positive or negative nurse intervention programrecommendation for each of the plurality of insurance claim transactionsinsurance claim transactions to which the predictive model has beenapplied, including a positive nurse intervention program recommendationfor at least one of the plurality of insurance claim transactions;responsive to a positive nurse intervention program recommendation,output data relating to the received insurance claim transaction; and anurse intervention program computer in communication with the nurseintervention program referral determination computer system andconfigured to receive the data relating to the received insurance claimtransaction output responsive to the positive nurse intervention programrecommendation.
 2. The computer system of claim 1, wherein the receiveddata indicative of an insurance claim transaction further comprisesprior injury data, and the predictive model is further responsive tovalues of prior injury data.
 3. The computer system of claim 1, whereinthe received data indicative of an insurance claim transaction furthercomprises reserve data, and the predictive model is further responsiveto values of reserve data.
 4. The computer system of claim 1, whereinthe program instructions, when executed by the one or more computerprocessors, further cause the one or more computer processors to: afterreceipt of the data indicative of an insurance claim transaction, andprior to applying the predictive model to the received data, determinewhether the insurance claim transaction is in a category that does notqualify for consideration for referral to a nurse intervention program;and apply the predictive model to the received data indicative of aninsurance claim transaction only responsive to determining that theinsurance claim transaction is not in a category that does not qualifyfor consideration for referral to a nurse intervention program.
 5. Thecomputer system of claim 4, wherein a category of insurance claimtransaction that does not qualify for consideration for referral to anurse intervention program comprises a workers compensation claimarising from loss of life on the job.
 6. The computer system of claim 1,wherein the program instructions, when executed by the one or morecomputer processors, further cause the one or more computer processorsto: determine a positive or negative nurse intervention programrecommendation by comparing the nurse intervention program score to athreshold value.
 7. The computer system of claim 1, wherein the programinstructions, when executed by the one or more computer processors,further cause the one or more computer processors to: include, in theoutput data, data causing the received insurance claim transaction to bequeued in a case manager queue according to the nurse interventionprogram score.
 8. The computer system of claim 1, wherein the insuranceclaim transactions are worker's compensation claims.
 9. The computersystem of claim 1, wherein the insurance claim transactions are bodilyinjury claims.